




'''simple docstring'''







import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_     						=				logging.get_logger(__name__)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     MobileNetVaConfig(layer_norm_eps=0.0_01						)

   if "_quant" in model_name:
      raise ValueError('Quantized models are not supported.'						)

   A__       =     re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'      ,UpperCAmelCase						)
   if matches:
      A__       =     float(matches[1]						)
      A__       =     int(matches[2]						)

   # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
   # the usual 1000. The first class (index 0) is "background".
   A__       =     1001
   A__       =     'imagenet-1k-id2label.json'
   A__       =     'huggingface/label-files'
   A__       =     json.load(open(hf_hub_download(UpperCAmelCase      ,UpperCAmelCase      ,repo_type='dataset'						)      ,'r'						)						)
   A__       =     {int(UpperCAmelCase						) + 1: v for k, v in idalabel.items()}
   A__       =     'background'
   A__       =     idalabel
   A__       =     {v: k for k, v in idalabel.items()}

   return config







def 						_A      ( ):






   '''simple docstring'''







   A__       =     'http://images.cocodataset.org/val2017/000000039769.jpg'
   A__       =     Image.open(requests.get(UpperCAmelCase      ,stream=UpperCAmelCase						).raw						)
   return im







@torch.no_grad()
def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=False						):






   '''simple docstring'''







   A__       =     get_mobilenet_va_config(UpperCAmelCase						)

   # Load 🤗 model
   A__       =     MobileNetVaForImageClassification(UpperCAmelCase						).eval()

   # Load weights from TensorFlow checkpoint
   load_tf_weights_in_mobilenet_va(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)

   # Check outputs on an image, prepared by MobileNetV1ImageProcessor
   A__       =     MobileNetVaImageProcessor(
       crop_size={'width': config.image_size, 'height': config.image_size}      ,size={'shortest_edge': config.image_size + 32}      ,)
   A__       =     image_processor(images=prepare_img()      ,return_tensors='pt'						)
   A__       =     model(**UpperCAmelCase						)
   A__       =     outputs.logits

   assert logits.shape == (1, 1001)

   if model_name == "mobilenet_v1_1.0_224":
      A__       =     torch.tensor([-4.17_39, -1.12_33, 3.12_05]						)
   elif model_name == "mobilenet_v1_0.75_192":
      A__       =     torch.tensor([-3.94_40, -2.31_41, -0.33_33]						)
   else:
      A__       =     None

   if expected_logits is not None:
      assert torch.allclose(logits[0, :3]      ,UpperCAmelCase      ,atol=1e-4						)

   Path(UpperCAmelCase						).mkdir(exist_ok=UpperCAmelCase						)
   print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""						)
   model.save_pretrained(UpperCAmelCase						)
   print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
   image_processor.save_pretrained(UpperCAmelCase						)

   if push_to_hub:
      print('Pushing to the hub...'						)
      A__       =     'google/' + model_name
      image_processor.push_to_hub(UpperCAmelCase						)
      model.push_to_hub(UpperCAmelCase						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--model_name''',
        default='''mobilenet_v1_1.0_224''',
        type=str,
        help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.''',
    )
    parser.add_argument(
        '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt file).'''
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
    )
    parser.add_argument(
        '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
    )

    lowerCAmelCase_     						=				parser.parse_args()
    convert_movilevit_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )








'''simple docstring'''







from torch import nn







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   if act_fn in ["swish", "silu"]:
      return nn.SiLU()
   elif act_fn == "mish":
      return nn.Mish()
   elif act_fn == "gelu":
      return nn.GELU()
   else:
      raise ValueError(F"""Unsupported activation function: {act_fn}"""						)


